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We have not really reached the peak of the AI bubble yet, so its a bit hard to concretely talk about moats. LLMs aren't the golden bullet the article hints at.

We have not really reached the peak of the AI bubble yet, so its a bit hard to concretely talk about moats. LLMs aren't the golden bullet the article hints at. Sure they are improving, but the cost is not falling. It costs a huge amount to create foundation models, and there will be a point where either we have a breakthrough (ie we move from sequence generation to concept synthesis) or the money runs out. But regardless the rule of thumb still holds: If your business idea is simple to do, then you need another plank to make your moat. That could be network effect, access to capital or both. Patents are there to inhibit capital, because it costs money to challenge a patent (as well as defend) If your business idea is not simple to implement, then you might have the benefit of time. AI doesn't really change any of that, it just amplifies the effect. ie, making an amazon clone is simple now, because the tech/infra exists. Amazon had to make that infrastructure first, which was hard.

beernet 2y ago
> LLMs aren't the golden bullet the article hints at. Sure they are improving, but the cost is not falling. It costs a huge amount to create foundation models, and there will be a point where either we have a breakthrough (ie we move from sequence generation to concept synthesis) or the money runs out. Very, very few use cases require training a new model. The vast majority can be solved by inferencing existing models, where it is absolutely true that inference costs are steadily declining. > making an amazon clone is simple now Seriously? Cloning Amazon is not equivalent to cloning a frontend...
- mnky9800n 2y ago
  Imo making it easy to specialise a model is hard right now. Building RAGs and other things like that requires technical knowledge but dumping things on ChatGPT does not. Perhaps building a ui for dummies for specialising models so that they actually learn from new data and not just do inference is the way to go. But I imagine perplexity et al. Is already trying to do this.
- KaiserPro 2y ago
  > Seriously? Cloning Amazon is not equivalent to cloning a frontend... A frontend isn't a business, something that a good number of startups forget. What I mean is that card payment, shipping, inventory management and sourcing are trivial compared to when amazon started.
silveraxe93 2y ago
But the cost is _definitely_ falling. For a recent example, see DeepSeek V3[1]. It's a model that's competitive with GPT-4, Claude Sonnet. But cost ~$6 Million to train. This is ridiculously cheaper than what we had before. Inference is basically getting an 10x cheaper per year! We're spending more because bigger models are worth the investment. But the "price per unit of [intelligence/quality]" is getting lower and _fast_. Saying that models are getting more expensive is confusing the absolute value spent with the value for money. - [1] https://github.com/deepseek-ai/DeepSeek-V3/tree/main https://github.com/deepseek-ai/DeepSeek-V3/tree/main
- ADeerAppeared 2y ago
  > Inference is basically getting an 10x cheaper per year! You're gonna need some good citations for that. There's a big difference between companies saying "The inference costs on our service are down" and the inference costs on the model are down. The former is oft cheated by simplyifying and dumbing down the models used in the service after the initial hype and benchmarks. > But the "price per unit of [intelligence/quality]" is getting lower and _fast_. Absolutely not a general trend across models. At best, older models are getting cheaper to run. Newer models are not cheaper "per unit of intelligence". OpenAI's fany new reasoning models are orders of magnitude more expensive to run whilst being ~linear improvements in real world capabilities.
  - silveraxe93 2y ago
    See situational-awareness[1], see the "algorithmic efficiencies" section. He shows many examples of how models are getting cheaper. With many citations. Costs are not just down on a specific service. Even though I don't see the problem in that, as long as you get the promised level of performance, without being subsidised. See the deepseek model I linked above. It's an open model and you can run it yourself. > At best, older models are getting cheaper to run. What's your definition of old here? If you compare the literal bleeding edge model (o3) to 2 years ago best model (GPT-4)? Not only is this a ridiculously misleading comparison, it's not even valid! o3 is a reasoning model. It can spend money at test time to improve results. Previous models don't even have this capability. You can't look at one example of where they just threw a lot of money and say this is the cost. The cost is unbounded! If they want, they can just not let the model think for ages and have basically "0-thinking" outputs. This is what you use to compare models. If you compare _todays_ cost for training and inference of a model as good as GPT-4 when it was released, this cost has massively gone down on both counts. [1] - https://situational-awareness.ai/from-gpt-4-to-agi/#The_trends_in_deep_learning https://situational-awareness.ai/from-gpt-4-to-agi/#The_tren...

beernet 2y ago

> LLMs aren't the golden bullet the article hints at. Sure they are improving, but the cost is not falling. It costs a huge amount to create foundation models, and there will be a point where either we have a breakthrough (ie we move from sequence generation to concept synthesis) or the money runs out. Very, very few use cases require training a new model. The vast majority can be solved by inferencing existing models, where it is absolutely true that inference costs are steadily declining. > making an amazon clone is simple now Seriously? Cloning Amazon is not equivalent to cloning a frontend...

mnky9800n 2y ago
Imo making it easy to specialise a model is hard right now. Building RAGs and other things like that requires technical knowledge but dumping things on ChatGPT does not. Perhaps building a ui for dummies for specialising models so that they actually learn from new data and not just do inference is the way to go. But I imagine perplexity et al. Is already trying to do this.
KaiserPro 2y ago
> Seriously? Cloning Amazon is not equivalent to cloning a frontend... A frontend isn't a business, something that a good number of startups forget. What I mean is that card payment, shipping, inventory management and sourcing are trivial compared to when amazon started.

silveraxe93 2y ago

But the cost is _definitely_ falling. For a recent example, see DeepSeek V3[1]. It's a model that's competitive with GPT-4, Claude Sonnet. But cost ~$6 Million to train. This is ridiculously cheaper than what we had before. Inference is basically getting an 10x cheaper per year! We're spending more because bigger models are worth the investment. But the "price per unit of [intelligence/quality]" is getting lower and _fast_. Saying that models are getting more expensive is confusing the absolute value spent with the value for money. - [1] https://github.com/deepseek-ai/DeepSeek-V3/tree/main https://github.com/deepseek-ai/DeepSeek-V3/tree/main

ADeerAppeared 2y ago
> Inference is basically getting an 10x cheaper per year! You're gonna need some good citations for that. There's a big difference between companies saying "The inference costs on our service are down" and the inference costs on the model are down. The former is oft cheated by simplyifying and dumbing down the models used in the service after the initial hype and benchmarks. > But the "price per unit of [intelligence/quality]" is getting lower and _fast_. Absolutely not a general trend across models. At best, older models are getting cheaper to run. Newer models are not cheaper "per unit of intelligence". OpenAI's fany new reasoning models are orders of magnitude more expensive to run whilst being ~linear improvements in real world capabilities.
- silveraxe93 2y ago
  See situational-awareness[1], see the "algorithmic efficiencies" section. He shows many examples of how models are getting cheaper. With many citations. Costs are not just down on a specific service. Even though I don't see the problem in that, as long as you get the promised level of performance, without being subsidised. See the deepseek model I linked above. It's an open model and you can run it yourself. > At best, older models are getting cheaper to run. What's your definition of old here? If you compare the literal bleeding edge model (o3) to 2 years ago best model (GPT-4)? Not only is this a ridiculously misleading comparison, it's not even valid! o3 is a reasoning model. It can spend money at test time to improve results. Previous models don't even have this capability. You can't look at one example of where they just threw a lot of money and say this is the cost. The cost is unbounded! If they want, they can just not let the model think for ages and have basically "0-thinking" outputs. This is what you use to compare models. If you compare _todays_ cost for training and inference of a model as good as GPT-4 when it was released, this cost has massively gone down on both counts. [1] - https://situational-awareness.ai/from-gpt-4-to-agi/#The_trends_in_deep_learning https://situational-awareness.ai/from-gpt-4-to-agi/#The_tren...